Machine Learning is Changing Developer Nation and the World

Breakthroughs and advancement in machine learning (ML) models, techniques, frameworks, and applications are having a growing influence on the developer ecosystem. Machine learning is enabling new experiences that allow computers to tackle more complex tasks that once only humans were able to. In the 14th edition of SlashData’s Developer Economics survey, the influence of machine learning can be observed throughout the collected data and analysis. Both the growth of new platforms and experiences as well changing infrastructure and programming languages are being driven by innovations in machine learning.

The State of the Developer Nation report 14th Edition summarizes some of the key findings from the survey and provides a snapshot of today’s developer ecosystem. Underlying these findings is the importance of machine learning is in driving future trends. As the capabilities of machines and humans begin to converge, implications extend well beyond the developer community. To be well positioned for future technology shifts, it is important to understand where and how machine learning is influencing the direction of the software industry.

ML is revolutionizing how we get around

52% percent of developers believe that advances in self-driving cars will have the most impact in the next five years. Machine learning is at the core of advancements in this area. The pivotal role that machine learning is playing in these projects is an indication of the power such advancements have in changing how people live their lives. Machine learning not only teaches cars to drive themselves but supports computer vision that can identify objects such as stop signs and pedestrians. The tremendous economics of this segment is attracting boatloads of capital that is leading to new advancements and new opportunities.

Machine Learning impacts AR

The growth of augmented reality is another area where ML will have an important impact. Deep learning is improving the simulation, localization, and mapping (SLAM) capabilities of leading AR platforms. SLAM enables AR platforms to identify objects and to overlay augmentations. It also recognizes and tracks features within a scene. These advancements are directly impacting the 15% of the developer community who are working on AR projects by supporting more advanced tools to create more fluid experiences. This takes the AR space beyond the Snapchat dogface mask. Given that mobile is the most popular platform for AR developers, 53% are targeting Android and 37% are targeting iOS, the developers in this space are also seeing a significant impact.

Image classification models are the #1 project that machine learning developers are working on so we expect continued advancement in this space to support driverless cars and AR. 22% percent of ML developers were working on image classification and object recognition. Another area where machine learning developers are working is conversational interfaces or natural language processing (NLP). 20% of ML developers were working on NLP/chatbots, ranking third in our survey of ML developers. Chatbots are already all the rage and NLP models have made significant advances. The next challenge is creating even more sophisticated models to make chatbots smarter.

ML drives Python and Serverless growth

While the work that machine learning developers are doing to create new experiences is having a profound effect on what developers can create, ML is also having a big influence on infrastructure and programming languages. Python rose to the third most popular language in our latest survey reaching 6.3 million developers behind JavaScript (9.7 million developers) and Java (7.3 million developers). Python supports many ML libraries and is easy to prototype and experiment with making it very popular with machine learning developers. The growth of serverless architectures is also being fueled by new machine learning models. While the development of models requires dedicated compute resources, serverless architectures can make implementing these models much easier. Not only can models easily be executed closest to the application but models can be tied together via functions that can span different languages and platforms, making applications even faster and smarter. Today the vast majority of workloads handled by serverless are web and mobile API calls but developers plan on using serverless for machine learning and conversational experiences more in the future.

The advancement and impact of ML is no news to developers. In our survey, 37% of developers believed that advancements in ML models would have the greatest impact over the next five years. Specifically, models that won’t require large training datasets, for example, using transfer learning or capsule networks. With less reliance on huge datasets, barriers to the exposition of new machine learning models are lowered and developers can create more models and smarter applications.

As the prevalence of machine learning grows, developers will need new skills that go beyond coding and computer science but incorporate, advanced mathematics, probability, statistics, and data modeling. Developers at the top of the food chain will be able to bring together skills, knowledge, and understanding from all these areas and apply them to next generation of problems.